How to define and capture Baselines in PostgreSQL Performance Troubleshooting?

Defining and capturing baselines is an essential part of performance troubleshooting in PostgreSQL. Baselines provide a reference point for normal performance metrics, against which you can compare current performance to identify anomalies, trends, or issues. Here's a structured approach to defining and capturing baselines for PostgreSQL performance troubleshooting:

1. Determine Key Performance Metrics

First, identify which metrics are crucial for understanding the health and performance of your PostgreSQL database. These typically include:
  • Query execution times: Average, median, and percentile execution times for your most critical queries.
  • Throughput: Transactions, queries, or operations per second.
  • Resource usage: CPU, memory, I/O utilization, and disk space usage.
  • Wait events: Frequency and duration of various wait events.
  • Connection statistics: Number of active and idle connections, and any connection pooling metrics.
  • Lock contention: Frequency and duration of lock waits.

2. Use PostgreSQL's Built-in Views for Data Collection

PostgreSQL provides several views that can be queried to collect baseline data:
  • pg_stat_activity: Shows current activity in the database, including active queries and wait events.
  • pg_stat_statements: Provides statistics on executed SQL statements (requires the pg_stat_statements extension to be enabled).
  • pg_stat_user_tables and pg_stat_user_indexes: Offer insights into table and index access patterns.

3. Establishing the Baseline

a. Define the Baseline Period

Choose a period of normal operation that represents typical usage patterns of your database. This might be a few hours, days, or even weeks, depending on the variability of your workload.

b. Capture Data

Collect data on the key performance metrics identified earlier. This can be done manually by running queries against the relevant PostgreSQL views at regular intervals, or automatically using monitoring tools.

c. Aggregate and Analyze

Aggregate the collected data to produce summary statistics for each metric. Analyze this data to establish average and peak values, identify patterns, and understand the normal range of variability for each metric.

4. Document the Baseline

Create a report or dashboard summarizing the baseline data. This should include not just the raw metrics, but also any insights or patterns observed during the baseline period. This documentation will be your reference point for future performance troubleshooting.

5. Implement Continuous Monitoring

Set up continuous monitoring to track the key performance metrics against the established baseline. Many tools and extensions can help with this, including:
  • External monitoring tools: Solutions like Grafana, Prometheus, or third-party SaaS monitoring platforms.
  • PostgreSQL extensions: Tools like pg_stat_statements for query analysis, and extensions that facilitate integration with external monitoring solutions.

6. Regularly Update the Baseline

As your database workload evolves, periodically review and update your performance baselines to ensure they remain relevant. Significant changes in application behavior, data volume, or infrastructure might necessitate a new baseline.


Capturing performance baselines is a proactive step in database administration that enables you to quickly identify deviations from normal performance, making it easier to diagnose and resolve issues. By understanding the normal operational profile of your PostgreSQL database, you can more effectively troubleshoot performance issues, plan for capacity, and ensure optimal performance over time.
About Shiv Iyer 456 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.